{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"LeNet.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"CavQCWld0j4f","colab_type":"code","colab":{}},"source":["import torch\n","import torchvision\n","import random\n","import numpy as np\n","\n","random.seed(0)\n","np.random.seed(0)\n","torch.manual_seed(0)\n","torch.cuda.manual_seed(0)\n","torch.backends.cudnn.deterministic = False"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"XfVpofkzJebW","colab_type":"code","colab":{}},"source":["import torchvision.datasets"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"PH5o91WHJszV","colab_type":"code","colab":{}},"source":["MNIST_train = torchvision.datasets.MNIST('/', download=True, train=True)\n","MNIST_test = torchvision.datasets.MNIST('/', download=True, train=False)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"q9_CCNBVKC3y","colab_type":"code","outputId":"978f5e63-ab2c-4e6a-fab6-69308229cbbd","executionInfo":{"status":"ok","timestamp":1569602934956,"user_tz":-180,"elapsed":619,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":173}},"source":["X_train = MNIST_train.train_data\n","y_train = MNIST_train.train_labels\n","\n","X_test = MNIST_test.test_data\n","y_test = MNIST_test.test_labels"],"execution_count":34,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:53: UserWarning: train_data has been renamed data\n","  warnings.warn(\"train_data has been renamed data\")\n","/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:43: UserWarning: train_labels has been renamed targets\n","  warnings.warn(\"train_labels has been renamed targets\")\n","/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:58: UserWarning: test_data has been renamed data\n","  warnings.warn(\"test_data has been renamed data\")\n","/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:48: UserWarning: test_labels has been renamed targets\n","  warnings.warn(\"test_labels has been renamed targets\")\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"sraWyU7XK3N-","colab_type":"code","outputId":"a87c45b8-57b9-4856-e6e5-ec6a550eaa87","executionInfo":{"status":"ok","timestamp":1569602935990,"user_tz":-180,"elapsed":1484,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["len(y_train), len(y_test)"],"execution_count":35,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(60000, 10000)"]},"metadata":{"tags":[]},"execution_count":35}]},{"cell_type":"code","metadata":{"id":"VeQ6niVBK55v","colab_type":"code","outputId":"a511d407-1933-45d0-cb26-4b98fcfddf21","executionInfo":{"status":"ok","timestamp":1569602936399,"user_tz":-180,"elapsed":1711,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["import matplotlib.pyplot as plt\n","\n","plt.imshow(X_train[0, :, :])\n","plt.show()\n","print(y_train[0])"],"execution_count":36,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["tensor(5)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"NnFvYbLlLRcx","colab_type":"code","outputId":"cb79ebc1-e13c-45b2-ff06-8e8daff9f720","executionInfo":{"status":"ok","timestamp":1569602936404,"user_tz":-180,"elapsed":1327,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["X_train.shape, X_test.shape"],"execution_count":37,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(torch.Size([60000, 28, 28]), torch.Size([10000, 28, 28]))"]},"metadata":{"tags":[]},"execution_count":37}]},{"cell_type":"code","metadata":{"id":"fjDl2m_vLFju","colab_type":"code","colab":{}},"source":["X_train = X_train.unsqueeze(1).float()   # добавляет батч\n","X_test = X_test.unsqueeze(1).float()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"DJVbfLeVLVNM","colab_type":"code","outputId":"faea2f5a-169b-43dc-de82-2fa55526a016","executionInfo":{"status":"ok","timestamp":1569602937309,"user_tz":-180,"elapsed":1442,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["X_train.shape, X_test.shape"],"execution_count":39,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(torch.Size([60000, 1, 28, 28]), torch.Size([10000, 1, 28, 28]))"]},"metadata":{"tags":[]},"execution_count":39}]},{"cell_type":"code","metadata":{"id":"mY3eDCyvLYJ1","colab_type":"code","colab":{}},"source":["class LeNet5(torch.nn.Module):\n","  def __init__(self):\n","    super().__init__()\n","    self.conv1 = torch.nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, padding=2)\n","    self.act1 = torch.nn.Tanh()\n","    self.pool1 = torch.nn.AvgPool2d(kernel_size=2, stride=2)\n","    \n","    self.conv2 = torch.nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, padding=0)\n","    self.act2 = torch.nn.Tanh()\n","    self.pool2 = torch.nn.AvgPool2d(kernel_size=2, stride=2)\n","    \n","    self.conv3 = torch.nn.Conv2d(in_channels=16, out_channels=120, kernel_size=5)\n","    self.act3 = torch.nn.Tanh()\n","    \n","    self.fc1 = torch.nn.Linear(120, 84)\n","    self.act4 = torch.nn.Tanh()\n","    \n","    self.fc2 = torch.nn.Linear(84, 10)\n","    \n","  def forward(self, x):\n","    x = self.conv1(x)\n","    x = self.act1(x)\n","    x = self.pool1(x)\n","    \n","    x = self.conv2(x)\n","    x = self.act2(x)\n","    x = self.pool2(x)\n","    \n","    x = self.conv3(x)\n","    x = self.act3(x)\n","    \n","    x = x.view(x.size(0), x.size(1) * x.size(2) * x.size(3))\n","    \n","    x = self.fc1(x)\n","    x = self.act4(x)\n","    x = self.fc2(x)\n","    \n","    return x\n","  \n","le_net5 = LeNet5()\n","    "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"qiQ-tadZSvPT","colab_type":"code","colab":{}},"source":["device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n","le_net5 = le_net5.to(device)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"6t_IVRtsUjq1","colab_type":"code","colab":{}},"source":["learning_rate = 0.001\n","loss = torch.nn.CrossEntropyLoss()\n","optimizer = torch.optim.Adam(le_net5.parameters(), lr=learning_rate)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ktMpStK5VVpP","colab_type":"code","outputId":"a09378f7-94e5-4b81-fce1-54cef1035dba","executionInfo":{"status":"ok","timestamp":1569607972779,"user_tz":-180,"elapsed":47,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["batch_size = 100\n","\n","train_loss_history = []\n","train_accuracy_history = []\n","\n","test_loss_history = []\n","test_accuracy_history = []\n","\n","X_test = X_test.to(device)\n","y_test = y_test.to(device)\n","\n","for epoch in range(1000):\n","  order = np.random.permutation(len(X_train))\n","  for start_index in range(0, len(X_train), batch_size):\n","    optimizer.zero_grad()\n","    \n","    batch_indices = order[start_index: start_index+batch_size]\n","    \n","    X_batch = X_train[batch_indices].to(device)\n","    y_batch = y_train[batch_indices].to(device)\n","    \n","    y_pred = le_net5.forward(X_batch)\n","    \n","    loss_value = loss(y_pred, y_batch)\n","    loss_value.backward()\n","    \n","    optimizer.step()\n","    \n","  y_pred_train = le_net5.forward(X_train.to(device))\n","  train_loss_history.append(loss(y_pred_train, y_train.to(device)).data.cpu())\n","  train_accuracy_history.append(\n","      (y_pred_train.argmax(dim=1) == y_train.to(device)).float().mean().data.cpu())\n","  \n","  y_pred_test = le_net5.forward(X_test)\n","  test_loss_history.append(loss(y_pred_test, y_test).data.cpu())\n","  test_accuracy_history.append(\n","      (y_pred_test.argmax(dim=1) == y_test).float().mean().data.cpu())\n","  \n","  if epoch % 100 == 0:\n","    print(test_loss_history[epoch], test_accuracy_history[epoch])\n","  \n","  \n","    "],"execution_count":43,"outputs":[{"output_type":"stream","text":["tensor(0.0775) tensor(0.9759)\n","tensor(0.0492) tensor(0.9900)\n","tensor(0.0656) tensor(0.9885)\n","tensor(0.0752) tensor(0.9903)\n","tensor(0.0841) tensor(0.9878)\n","tensor(0.0704) tensor(0.9918)\n","tensor(0.0826) tensor(0.9888)\n","tensor(0.0737) tensor(0.9895)\n","tensor(0.1080) tensor(0.9880)\n","tensor(0.0861) tensor(0.9887)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"N-i0nXg_nOtT","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":286},"outputId":"d38476f1-ce21-4ea9-ad83-dd04ae5630f9","executionInfo":{"status":"ok","timestamp":1569608024109,"user_tz":-180,"elapsed":2245,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}}},"source":["plt.plot(train_loss_history, label='train')\n","plt.plot(test_loss_history, label='test')\n","plt.legend()"],"execution_count":47,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7f6ea4806780>"]},"metadata":{"tags":[]},"execution_count":47},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAX0AAAD8CAYAAACb4nSYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nr6joWSMyEJoX1P50WvqxfNTFnxgXzuifhfyy2XnRp4yLFlIZrAsP2XRa+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lqsfCGj8CX6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v6avokJ/Nbss6vu64Abw8ax35OVn120YVdiE3kMWXxaG+ou7t8+o75Du1yaFjQQ7ZMVwj32/f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\nRK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"A7-4ZVibb6B_","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":286},"outputId":"f0a278bf-89ff-42ad-ef09-a3fc6fe5b0f0","executionInfo":{"status":"ok","timestamp":1569608269302,"user_tz":-180,"elapsed":34,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}}},"source":["plt.plot(train_accuracy_history, label='train')\n","plt.plot(test_accuracy_history, label='test', )\n","plt.legend()"],"execution_count":53,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 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FD8fSs/r7l2KEoayZgh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gHHZNbbdAURRlnycjnMqKoihKclQgKIqy31OXfKVVoarXGUggiMhYEVkoIktE5Eaf/R1EZLqI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\nQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"nAMmWj617i6c","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"9088dcba-a56c-47ec-d8cd-634ce4e0772e","executionInfo":{"status":"ok","timestamp":1569608465391,"user_tz":-180,"elapsed":902,"user":{"displayName":"Вадим Ахметов","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDNoWzT17ekzOOn-y-NdYIp3sp-wpGf4zMOlVM3Bw=s64","userId":"10121109767723331000"}}},"source":["print('эпоха на которой достигается максимальная точность:', \n","      test_accuracy_history.index(max(test_accuracy_history)),\n","      ', со значением:', max(test_accuracy_history))"],"execution_count":61,"outputs":[{"output_type":"stream","text":["эпоха на которой достигается максимальная точность: 502 , со значением: tensor(0.9920)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"VJZvo5rN8P0X","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}